{
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  "metadata": {
    "colab": {
      "name": "FinRL_stock_trading_fundamental",
      "provenance": [],
      "collapsed_sections": [
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        "MRiOtrywfAo1",
        "_gDkU-j-fCmZ",
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.12"
    },
    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mariko-sawada/FinRL/blob/master/FinRL_stock_trading_fundamental.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog uses FinRL to reproduce the paper: Practical Deep Reinforcement Learning Approach for Stock Trading, Workshop on Challenges and Opportunities for AI in Financial Services, NeurIPS 2018.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPT0ipYE28wL",
        "outputId": "a4a7bf9f-2b25-4ba4-f183-7570e75f8ac3"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.4.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.1.1)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-cp37-none-any.whl size=39043 sha256=f85a879d929c3caf6ecbd4fbf63dcd416df3dad780ab448d13e82756554a6080\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-iqfagg54/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.60-py2.py3-none-any.whl size=23819 sha256=b4e324e54d77e5735820b8151cf99b5ea8adb9fa91da739292d3ea87be8a3958\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/be/a4/846f02c5985562250917b0ab7b33fff737c8e6e8cd5209aa3b\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75776 sha256=e61d41ffc12085bff64205a42be90e241a3794ecb1bbcc7b4f3a4bd3b3c3067b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-iqfagg54/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39780 sha256=859d500928f774c8f91bc9c529243312f944ac2a72adc1ebade9004a9d920522\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.1.0 stockstats-0.3.2 yfinance-0.1.60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPqeTTwoh1hn",
        "outputId": "a29cc1a0-458e-48be-8559-609777153799"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "346ddb82-8b25-4bf8-b2be-25c5ab30da25"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "9b078835-7979-41ed-a99e-75b6d977e16d"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "8177626e-cc37-4397-f7ef-e536679233f2"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "a1070c5e-6911-4910-b38f-730d92129825"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (90630, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "28298530-617a-4534-ae33-6bc764e66298"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90630, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRWscKiPXXnj"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'],format='%Y-%m-%d')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "98527fc8-441d-41e9-a544-28b9fef1acf0"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date       open       high        low      close     volume   tic  day\n",
              "0 2009-01-02   3.067143   3.251429   3.041429   2.787006  746015200  AAPL    4\n",
              "1 2009-01-02  18.570000  19.520000  18.400000  15.657365   10955700   AXP    4\n",
              "2 2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3 2009-01-02  44.910000  46.980000  44.709999  32.830360    7117200   CAT    4\n",
              "4 2009-01-02  16.410000  17.000000  16.250000  12.505757   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-H6amcKXnTR",
        "outputId": "e5051cce-0d7c-4530-cce2-96d105bbb52b"
      },
      "source": [
        "type(df['date'][0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess fundamental data\n",
        "- Preprocess fundamental data and calculate financial ratios\n",
        "- Add those ratios to the price data\n",
        "- Calculate price-related ratios such as P/E and P/B"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmKP-1ii3RLS",
        "outputId": "abf1b3c6-db1d-4cd9-9d2c-e0c4bc06ab98"
      },
      "source": [
        "# Import fundamental data from my GitHub repository\n",
        "url = 'https://raw.githubusercontent.com/mariko-sawada/FinRL_with_fundamental_data/main/dow_30_fundamental_wrds.csv'\n",
        "\n",
        "fund = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (16,25) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Tslhs_O5pOTL",
        "outputId": "35bd1a15-edf2-437b-9605-6ec3a3e2394b"
      },
      "source": [
        "fund.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>popsrc</th>\n",
              "      <th>datafmt</th>\n",
              "      <th>tic</th>\n",
              "      <th>conm</th>\n",
              "      <th>acctchgq</th>\n",
              "      <th>acctstdq</th>\n",
              "      <th>adrrq</th>\n",
              "      <th>ajexq</th>\n",
              "      <th>ajpq</th>\n",
              "      <th>bsprq</th>\n",
              "      <th>compstq</th>\n",
              "      <th>curcdq</th>\n",
              "      <th>curncdq</th>\n",
              "      <th>currtrq</th>\n",
              "      <th>curuscnq</th>\n",
              "      <th>datacqtr</th>\n",
              "      <th>datafqtr</th>\n",
              "      <th>finalq</th>\n",
              "      <th>ogmq</th>\n",
              "      <th>rp</th>\n",
              "      <th>scfq</th>\n",
              "      <th>srcq</th>\n",
              "      <th>staltq</th>\n",
              "      <th>updq</th>\n",
              "      <th>apdedateq</th>\n",
              "      <th>fdateq</th>\n",
              "      <th>pdateq</th>\n",
              "      <th>rdq</th>\n",
              "      <th>acchgq</th>\n",
              "      <th>acomincq</th>\n",
              "      <th>acoq</th>\n",
              "      <th>actq</th>\n",
              "      <th>altoq</th>\n",
              "      <th>...</th>\n",
              "      <th>uspiy</th>\n",
              "      <th>ustdncy</th>\n",
              "      <th>usubdvpy</th>\n",
              "      <th>utfdocy</th>\n",
              "      <th>utfoscy</th>\n",
              "      <th>utmey</th>\n",
              "      <th>uwkcapcy</th>\n",
              "      <th>wcapchy</th>\n",
              "      <th>wcapcy</th>\n",
              "      <th>wday</th>\n",
              "      <th>wddy</th>\n",
              "      <th>wdepsy</th>\n",
              "      <th>wdpy</th>\n",
              "      <th>xidocy</th>\n",
              "      <th>xidoy</th>\n",
              "      <th>xinty</th>\n",
              "      <th>xiy</th>\n",
              "      <th>xopry</th>\n",
              "      <th>xoptdqpy</th>\n",
              "      <th>xoptdy</th>\n",
              "      <th>xoptepsqpy</th>\n",
              "      <th>xoptepsy</th>\n",
              "      <th>xoptqpy</th>\n",
              "      <th>xopty</th>\n",
              "      <th>xrdy</th>\n",
              "      <th>xsgay</th>\n",
              "      <th>exchg</th>\n",
              "      <th>costat</th>\n",
              "      <th>cshtrq</th>\n",
              "      <th>dvpspq</th>\n",
              "      <th>dvpsxq</th>\n",
              "      <th>mkvaltq</th>\n",
              "      <th>prccq</th>\n",
              "      <th>prchq</th>\n",
              "      <th>prclq</th>\n",
              "      <th>adjex</th>\n",
              "      <th>ggroup</th>\n",
              "      <th>gind</th>\n",
              "      <th>gsector</th>\n",
              "      <th>gsubind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1447</td>\n",
              "      <td>19990630</td>\n",
              "      <td>1999</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q2</td>\n",
              "      <td>1999Q2</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19990726.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9125.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>106125400.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>130.1250</td>\n",
              "      <td>142.6250</td>\n",
              "      <td>114.5000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1447</td>\n",
              "      <td>19990930</td>\n",
              "      <td>1999</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q3</td>\n",
              "      <td>1999Q3</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19991025.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13803.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>121724600.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0000</td>\n",
              "      <td>150.6250</td>\n",
              "      <td>121.8750</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1447</td>\n",
              "      <td>19991231</td>\n",
              "      <td>1999</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1999Q4</td>\n",
              "      <td>1999Q4</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18967.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>126218100.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>166.2500</td>\n",
              "      <td>168.8750</td>\n",
              "      <td>130.2500</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1447</td>\n",
              "      <td>20000331</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000Q1</td>\n",
              "      <td>2000Q1</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000424.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5101.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>167224700.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>148.9375</td>\n",
              "      <td>169.5000</td>\n",
              "      <td>119.5000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1447</td>\n",
              "      <td>20000630</td>\n",
              "      <td>2000</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>INDL</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>STD</td>\n",
              "      <td>AXP</td>\n",
              "      <td>AMERICAN EXPRESS CO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>GB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD</td>\n",
              "      <td>USD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000Q2</td>\n",
              "      <td>2000Q2</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000724.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10425.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>A</td>\n",
              "      <td>325319600.0</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.1250</td>\n",
              "      <td>57.1875</td>\n",
              "      <td>43.9375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4020</td>\n",
              "      <td>402020</td>\n",
              "      <td>40</td>\n",
              "      <td>40202010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 647 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   gvkey  datadate  fyearq  fqtr  fyr  ... adjex ggroup    gind gsector   gsubind\n",
              "0   1447  19990630    1999     2   12  ...   3.0   4020  402020      40  40202010\n",
              "1   1447  19990930    1999     3   12  ...   3.0   4020  402020      40  40202010\n",
              "2   1447  19991231    1999     4   12  ...   3.0   4020  402020      40  40202010\n",
              "3   1447  20000331    2000     1   12  ...   3.0   4020  402020      40  40202010\n",
              "4   1447  20000630    2000     2   12  ...   1.0   4020  402020      40  40202010\n",
              "\n",
              "[5 rows x 647 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD0kFC7Ap02K"
      },
      "source": [
        "# List items that are used to calculate financial ratios\n",
        "\n",
        "items = [\n",
        "    'datadate', # Date\n",
        "    'tic', # Ticker\n",
        "    'oiadpq', # Quarterly operating income\n",
        "    'revtq', # Quartely revenue\n",
        "    'niq', # Quartely net income\n",
        "    'atq', # Total asset\n",
        "    'teqq', # Shareholder's equity\n",
        "    'epspiy', # EPS(Basic) incl. Extraordinary items\n",
        "    'ceqq', # Common Equity\n",
        "    'cshoq', # Common Shares Outstanding\n",
        "    'dvpspq', # Dividends per share\n",
        "    'actq', # Current assets\n",
        "    'lctq', # Current liabilities\n",
        "    'cheq', # Cash & Equivalent\n",
        "    'rectq', # Recievalbles\n",
        "    'cogsq', # Cost of  Goods Sold\n",
        "    'invtq', # Inventories\n",
        "    'apq',# Account payable\n",
        "    'dlttq', # Long term debt\n",
        "    'dlcq', # Debt in current liabilites\n",
        "    'ltq' # Liabilities   \n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGOthD6vp6Sw"
      },
      "source": [
        "fund_data = fund[items]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jE7UNYtIqFkv"
      },
      "source": [
        "fund_data = fund_data.rename(columns={\n",
        "    'datadate':'date', # Date\n",
        "    'oiadpq':'op_inc_q', # Quarterly operating income\n",
        "    'revtq':'rev_q', # Quartely revenue\n",
        "    'niq':'net_inc_q', # Quartely net income\n",
        "    'atq':'tot_assets', # Assets\n",
        "    'teqq':'sh_equity', # Shareholder's equity\n",
        "    'epspiy':'eps_incl_ex', # EPS(Basic) incl. Extraordinary items\n",
        "    'ceqq':'com_eq', # Common Equity\n",
        "    'cshoq':'sh_outstanding', # Common Shares Outstanding\n",
        "    'dvpspq':'div_per_sh', # Dividends per share\n",
        "    'actq':'cur_assets', # Current assets\n",
        "    'lctq':'cur_liabilities', # Current liabilities\n",
        "    'cheq':'cash_eq', # Cash & Equivalent\n",
        "    'rectq':'receivables', # Receivalbles\n",
        "    'cogsq':'cogs_q', # Cost of  Goods Sold\n",
        "    'invtq':'inventories', # Inventories\n",
        "    'apq': 'payables',# Account payable\n",
        "    'dlttq':'long_debt', # Long term debt\n",
        "    'dlcq':'short_debt', # Debt in current liabilites\n",
        "    'ltq':'tot_liabilities' # Liabilities   \n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "A0sszApfqO6D",
        "outputId": "439f24d9-e951-459d-caa9-71b5c1e64d05"
      },
      "source": [
        "fund_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>op_inc_q</th>\n",
              "      <th>rev_q</th>\n",
              "      <th>net_inc_q</th>\n",
              "      <th>tot_assets</th>\n",
              "      <th>sh_equity</th>\n",
              "      <th>eps_incl_ex</th>\n",
              "      <th>com_eq</th>\n",
              "      <th>sh_outstanding</th>\n",
              "      <th>div_per_sh</th>\n",
              "      <th>cur_assets</th>\n",
              "      <th>cur_liabilities</th>\n",
              "      <th>cash_eq</th>\n",
              "      <th>receivables</th>\n",
              "      <th>cogs_q</th>\n",
              "      <th>inventories</th>\n",
              "      <th>payables</th>\n",
              "      <th>long_debt</th>\n",
              "      <th>short_debt</th>\n",
              "      <th>tot_liabilities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19990630</td>\n",
              "      <td>AXP</td>\n",
              "      <td>896.0</td>\n",
              "      <td>5564.0</td>\n",
              "      <td>646.0</td>\n",
              "      <td>132452.0</td>\n",
              "      <td>9762.0</td>\n",
              "      <td>2.73</td>\n",
              "      <td>9762.0</td>\n",
              "      <td>449.0</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6096.0</td>\n",
              "      <td>46774.0</td>\n",
              "      <td>4668.0</td>\n",
              "      <td>448.0</td>\n",
              "      <td>22282.0</td>\n",
              "      <td>7005.0</td>\n",
              "      <td>24785.0</td>\n",
              "      <td>122690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19990930</td>\n",
              "      <td>AXP</td>\n",
              "      <td>906.0</td>\n",
              "      <td>5584.0</td>\n",
              "      <td>648.0</td>\n",
              "      <td>132616.0</td>\n",
              "      <td>9744.0</td>\n",
              "      <td>4.18</td>\n",
              "      <td>9744.0</td>\n",
              "      <td>447.6</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5102.0</td>\n",
              "      <td>48827.0</td>\n",
              "      <td>4678.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>23587.0</td>\n",
              "      <td>6720.0</td>\n",
              "      <td>24683.0</td>\n",
              "      <td>122872.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19991231</td>\n",
              "      <td>AXP</td>\n",
              "      <td>845.0</td>\n",
              "      <td>6009.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>148517.0</td>\n",
              "      <td>10095.0</td>\n",
              "      <td>5.54</td>\n",
              "      <td>10095.0</td>\n",
              "      <td>446.9</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10391.0</td>\n",
              "      <td>54033.0</td>\n",
              "      <td>5164.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>25719.0</td>\n",
              "      <td>4685.0</td>\n",
              "      <td>32437.0</td>\n",
              "      <td>138422.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20000331</td>\n",
              "      <td>AXP</td>\n",
              "      <td>920.0</td>\n",
              "      <td>6021.0</td>\n",
              "      <td>656.0</td>\n",
              "      <td>150662.0</td>\n",
              "      <td>10253.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>10253.0</td>\n",
              "      <td>444.7</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7425.0</td>\n",
              "      <td>53663.0</td>\n",
              "      <td>5101.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>26379.0</td>\n",
              "      <td>5670.0</td>\n",
              "      <td>29342.0</td>\n",
              "      <td>140409.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20000630</td>\n",
              "      <td>AXP</td>\n",
              "      <td>1046.0</td>\n",
              "      <td>6370.0</td>\n",
              "      <td>740.0</td>\n",
              "      <td>148553.0</td>\n",
              "      <td>10509.0</td>\n",
              "      <td>1.05</td>\n",
              "      <td>10509.0</td>\n",
              "      <td>1333.0</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6841.0</td>\n",
              "      <td>54286.0</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>29536.0</td>\n",
              "      <td>5336.0</td>\n",
              "      <td>26170.0</td>\n",
              "      <td>138044.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date  tic  op_inc_q  ...  long_debt  short_debt  tot_liabilities\n",
              "0  19990630  AXP     896.0  ...     7005.0     24785.0         122690.0\n",
              "1  19990930  AXP     906.0  ...     6720.0     24683.0         122872.0\n",
              "2  19991231  AXP     845.0  ...     4685.0     32437.0         138422.0\n",
              "3  20000331  AXP     920.0  ...     5670.0     29342.0         140409.0\n",
              "4  20000630  AXP    1046.0  ...     5336.0     26170.0         138044.0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfWtEophqS33",
        "outputId": "682762b0-22fe-4d1a-bc6a-66207cb2ad56"
      },
      "source": [
        "# Calculate financial ratios\n",
        "date = pd.to_datetime(fund_data['date'],format='%Y%m%d')\n",
        "\n",
        "tic = fund_data['tic'].to_frame('tic')\n",
        "\n",
        "OPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='OPM')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        OPM[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        OPM.iloc[i] = np.nan\n",
        "    else:\n",
        "        OPM.iloc[i] = np.sum(fund_data['op_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
        "\n",
        "        \n",
        "NPM = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='NPM')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        NPM[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        NPM.iloc[i] = np.nan\n",
        "    else:\n",
        "        NPM.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/np.sum(fund_data['rev_q'].iloc[i-3:i])\n",
        "\n",
        "ROA = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROA')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        ROA[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        ROA.iloc[i] = np.nan\n",
        "    else:\n",
        "        ROA.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['tot_assets'].iloc[i]\n",
        "\n",
        "ROE = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='ROE')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        ROE[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        ROE.iloc[i] = np.nan\n",
        "    else:\n",
        "        ROE.iloc[i] = np.sum(fund_data['net_inc_q'].iloc[i-3:i])/fund_data['sh_equity'].iloc[i]        \n",
        "        \n",
        "EPS = fund_data['eps_incl_ex'].to_frame('EPS')\n",
        "BPS = (fund_data['com_eq']/fund_data['sh_outstanding']).to_frame('BPS') # Need to check units\n",
        "DPS = fund_data['div_per_sh'].to_frame('DPS')\n",
        "cur_ratio = (fund_data['cur_assets']/fund_data['cur_liabilities']).to_frame('cur_ratio')\n",
        "quick_ratio = ((fund_data['cash_eq'] + fund_data['receivables'] )/fund_data['cur_liabilities']).to_frame('quick_ratio')\n",
        "cash_ratio = (fund_data['cash_eq']/fund_data['cur_liabilities']).to_frame('cash_ratio')\n",
        "\n",
        "inv_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='inv_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        inv_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        inv_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        inv_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['inventories'].iloc[i]\n",
        "        \n",
        "acc_rec_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_rec_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        acc_rec_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        acc_rec_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        acc_rec_turnover.iloc[i] = np.sum(fund_data['rev_q'].iloc[i-3:i])/fund_data['receivables'].iloc[i]\n",
        "\n",
        "acc_pay_turnover = pd.Series(np.empty(fund_data.shape[0],dtype=object),name='acc_pay_turnover')\n",
        "for i in range(0, fund_data.shape[0]):\n",
        "    if i-3 < 0:\n",
        "        acc_pay_turnover[i] = np.nan\n",
        "    elif fund_data.iloc[i,1] != fund_data.iloc[i-3,1]:\n",
        "        acc_pay_turnover.iloc[i] = np.nan\n",
        "    else:\n",
        "        acc_pay_turnover.iloc[i] = np.sum(fund_data['cogs_q'].iloc[i-3:i])/fund_data['payables'].iloc[i]\n",
        "        \n",
        "\n",
        "debt_ratio = (fund_data['tot_liabilities']/fund_data['tot_assets']).to_frame('debt_ratio')\n",
        "debt_to_equity = (fund_data['tot_liabilities']/fund_data['sh_equity']).to_frame('debt_to_equity')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:57: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:57: RuntimeWarning: invalid value encountered in double_scalars\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwFVopRDqcby"
      },
      "source": [
        "# Create a dataframe that merges all the ratios\n",
        "ratios = pd.concat([date,tic,OPM,NPM,ROA,ROE,EPS,BPS,DPS,\n",
        "                    cur_ratio,quick_ratio,cash_ratio,inv_turnover,acc_rec_turnover,acc_pay_turnover,\n",
        "                   debt_ratio,debt_to_equity], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Mvnw7izFsJcT",
        "outputId": "8a01a588-03fc-4026-ee4b-c6eebcd529af"
      },
      "source": [
        "ratios"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.73</td>\n",
              "      <td>21.741648</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.926298</td>\n",
              "      <td>12.568121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999-09-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.18</td>\n",
              "      <td>21.769437</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.926525</td>\n",
              "      <td>12.610016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999-12-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.54</td>\n",
              "      <td>22.588946</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.932028</td>\n",
              "      <td>13.711937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-03-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.154281</td>\n",
              "      <td>0.110742</td>\n",
              "      <td>0.012611</td>\n",
              "      <td>0.185312</td>\n",
              "      <td>1.48</td>\n",
              "      <td>23.055993</td>\n",
              "      <td>0.225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46.0635</td>\n",
              "      <td>0.319717</td>\n",
              "      <td>0.550059</td>\n",
              "      <td>0.931947</td>\n",
              "      <td>13.694431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.151641</td>\n",
              "      <td>0.108436</td>\n",
              "      <td>0.0128574</td>\n",
              "      <td>0.181749</td>\n",
              "      <td>1.05</td>\n",
              "      <td>7.883721</td>\n",
              "      <td>0.080</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.2529</td>\n",
              "      <td>0.324467</td>\n",
              "      <td>0.505925</td>\n",
              "      <td>0.929258</td>\n",
              "      <td>13.135788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2451</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.667517</td>\n",
              "      <td>0.521213</td>\n",
              "      <td>0.129058</td>\n",
              "      <td>0.271736</td>\n",
              "      <td>2.85</td>\n",
              "      <td>13.647142</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1.248714</td>\n",
              "      <td>1.140070</td>\n",
              "      <td>0.955150</td>\n",
              "      <td>inf</td>\n",
              "      <td>6.11635</td>\n",
              "      <td>2.69754</td>\n",
              "      <td>0.525062</td>\n",
              "      <td>1.105537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.668385</td>\n",
              "      <td>0.519867</td>\n",
              "      <td>0.120448</td>\n",
              "      <td>0.264075</td>\n",
              "      <td>3.92</td>\n",
              "      <td>14.203947</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1.553478</td>\n",
              "      <td>1.443292</td>\n",
              "      <td>1.221925</td>\n",
              "      <td>inf</td>\n",
              "      <td>5.06313</td>\n",
              "      <td>1.88951</td>\n",
              "      <td>0.543886</td>\n",
              "      <td>1.192433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.654464</td>\n",
              "      <td>0.52129</td>\n",
              "      <td>0.107873</td>\n",
              "      <td>0.241066</td>\n",
              "      <td>4.90</td>\n",
              "      <td>14.653484</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1.905238</td>\n",
              "      <td>1.784838</td>\n",
              "      <td>1.579807</td>\n",
              "      <td>inf</td>\n",
              "      <td>5.62857</td>\n",
              "      <td>2.73037</td>\n",
              "      <td>0.552515</td>\n",
              "      <td>1.234714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>0.480876</td>\n",
              "      <td>0.0944222</td>\n",
              "      <td>0.201545</td>\n",
              "      <td>1.42</td>\n",
              "      <td>15.908283</td>\n",
              "      <td>0.320</td>\n",
              "      <td>2.121065</td>\n",
              "      <td>1.969814</td>\n",
              "      <td>1.700081</td>\n",
              "      <td>inf</td>\n",
              "      <td>4.72531</td>\n",
              "      <td>2.34787</td>\n",
              "      <td>0.531507</td>\n",
              "      <td>1.134505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.640128</td>\n",
              "      <td>0.488704</td>\n",
              "      <td>0.0952179</td>\n",
              "      <td>0.202568</td>\n",
              "      <td>2.80</td>\n",
              "      <td>16.088525</td>\n",
              "      <td>0.320</td>\n",
              "      <td>2.116356</td>\n",
              "      <td>1.954292</td>\n",
              "      <td>1.700574</td>\n",
              "      <td>inf</td>\n",
              "      <td>4.84496</td>\n",
              "      <td>2.36736</td>\n",
              "      <td>0.529946</td>\n",
              "      <td>1.127414</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2456 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  tic       OPM  ... acc_pay_turnover debt_ratio debt_to_equity\n",
              "0    1999-06-30  AXP       NaN  ...              NaN   0.926298      12.568121\n",
              "1    1999-09-30  AXP       NaN  ...              NaN   0.926525      12.610016\n",
              "2    1999-12-31  AXP       NaN  ...              NaN   0.932028      13.711937\n",
              "3    2000-03-31  AXP  0.154281  ...         0.550059   0.931947      13.694431\n",
              "4    2000-06-30  AXP  0.151641  ...         0.505925   0.929258      13.135788\n",
              "...         ...  ...       ...  ...              ...        ...            ...\n",
              "2451 2020-03-31    V  0.667517  ...          2.69754   0.525062       1.105537\n",
              "2452 2020-06-30    V  0.668385  ...          1.88951   0.543886       1.192433\n",
              "2453 2020-09-30    V  0.654464  ...          2.73037   0.552515       1.234714\n",
              "2454 2020-12-31    V  0.638994  ...          2.34787   0.531507       1.134505\n",
              "2455 2021-03-31    V  0.640128  ...          2.36736   0.529946       1.127414\n",
              "\n",
              "[2456 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuKPlGe4sNzQ"
      },
      "source": [
        "# Replace NAs with zero and replace infinity values with zero\n",
        "final_ratios = ratios.copy()\n",
        "final_ratios = final_ratios.fillna(0)\n",
        "final_ratios = final_ratios.replace(np.inf,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wc_rvvm1sRDd",
        "outputId": "a2056df0-3c57-4abe-c7e0-5cc3d0ba62f1"
      },
      "source": [
        "final_ratios.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.926298</td>\n",
              "      <td>12.568121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999-09-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.18</td>\n",
              "      <td>21.769437</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.926525</td>\n",
              "      <td>12.610016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999-12-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.54</td>\n",
              "      <td>22.588946</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.932028</td>\n",
              "      <td>13.711937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-03-31</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.154281</td>\n",
              "      <td>0.110742</td>\n",
              "      <td>0.012611</td>\n",
              "      <td>0.185312</td>\n",
              "      <td>1.48</td>\n",
              "      <td>23.055993</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.063492</td>\n",
              "      <td>0.319717</td>\n",
              "      <td>0.550059</td>\n",
              "      <td>0.931947</td>\n",
              "      <td>13.694431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-06-30</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.151641</td>\n",
              "      <td>0.108436</td>\n",
              "      <td>0.012857</td>\n",
              "      <td>0.181749</td>\n",
              "      <td>1.05</td>\n",
              "      <td>7.883721</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.252874</td>\n",
              "      <td>0.324467</td>\n",
              "      <td>0.505925</td>\n",
              "      <td>0.929258</td>\n",
              "      <td>13.135788</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  tic       OPM  ...  acc_pay_turnover  debt_ratio  debt_to_equity\n",
              "0 1999-06-30  AXP  0.000000  ...          0.000000    0.926298       12.568121\n",
              "1 1999-09-30  AXP  0.000000  ...          0.000000    0.926525       12.610016\n",
              "2 1999-12-31  AXP  0.000000  ...          0.000000    0.932028       13.711937\n",
              "3 2000-03-31  AXP  0.154281  ...          0.550059    0.931947       13.694431\n",
              "4 2000-06-30  AXP  0.151641  ...          0.505925    0.929258       13.135788\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "RKwmRfs5sfra",
        "outputId": "e9a68494-e293-4c5a-fda6-22ee5377b8f1"
      },
      "source": [
        "final_ratios.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>EPS</th>\n",
              "      <th>BPS</th>\n",
              "      <th>DPS</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2451</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.667517</td>\n",
              "      <td>0.521213</td>\n",
              "      <td>0.129058</td>\n",
              "      <td>0.271736</td>\n",
              "      <td>2.85</td>\n",
              "      <td>13.647142</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.248714</td>\n",
              "      <td>1.140070</td>\n",
              "      <td>0.955150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.116350</td>\n",
              "      <td>2.697537</td>\n",
              "      <td>0.525062</td>\n",
              "      <td>1.105537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.668385</td>\n",
              "      <td>0.519867</td>\n",
              "      <td>0.120448</td>\n",
              "      <td>0.264075</td>\n",
              "      <td>3.92</td>\n",
              "      <td>14.203947</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.553478</td>\n",
              "      <td>1.443292</td>\n",
              "      <td>1.221925</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.063131</td>\n",
              "      <td>1.889507</td>\n",
              "      <td>0.543886</td>\n",
              "      <td>1.192433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2453</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>V</td>\n",
              "      <td>0.654464</td>\n",
              "      <td>0.521290</td>\n",
              "      <td>0.107873</td>\n",
              "      <td>0.241066</td>\n",
              "      <td>4.90</td>\n",
              "      <td>14.653484</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.905238</td>\n",
              "      <td>1.784838</td>\n",
              "      <td>1.579807</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.628571</td>\n",
              "      <td>2.730366</td>\n",
              "      <td>0.552515</td>\n",
              "      <td>1.234714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.638994</td>\n",
              "      <td>0.480876</td>\n",
              "      <td>0.094422</td>\n",
              "      <td>0.201545</td>\n",
              "      <td>1.42</td>\n",
              "      <td>15.908283</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.121065</td>\n",
              "      <td>1.969814</td>\n",
              "      <td>1.700081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.725314</td>\n",
              "      <td>2.347866</td>\n",
              "      <td>0.531507</td>\n",
              "      <td>1.134505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2021-03-31</td>\n",
              "      <td>V</td>\n",
              "      <td>0.640128</td>\n",
              "      <td>0.488704</td>\n",
              "      <td>0.095218</td>\n",
              "      <td>0.202568</td>\n",
              "      <td>2.80</td>\n",
              "      <td>16.088525</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.116356</td>\n",
              "      <td>1.954292</td>\n",
              "      <td>1.700574</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.844961</td>\n",
              "      <td>2.367357</td>\n",
              "      <td>0.529946</td>\n",
              "      <td>1.127414</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date tic       OPM  ...  acc_pay_turnover  debt_ratio  debt_to_equity\n",
              "2451 2020-03-31   V  0.667517  ...          2.697537    0.525062        1.105537\n",
              "2452 2020-06-30   V  0.668385  ...          1.889507    0.543886        1.192433\n",
              "2453 2020-09-30   V  0.654464  ...          2.730366    0.552515        1.234714\n",
              "2454 2020-12-31   V  0.638994  ...          2.347866    0.531507        1.134505\n",
              "2455 2021-03-31   V  0.640128  ...          2.367357    0.529946        1.127414\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "source": [
        "list_ticker = df[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(df['date'].min(),df['date'].max()))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "# Merge stock price data and ratios into one dataframe\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(df,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full.merge(final_ratios,how='left',on=['date','tic'])\n",
        "processed_full = processed_full.sort_values(['tic','date'])\n",
        "\n",
        "# Backfill the ratio data to make them daily\n",
        "processed_full = processed_full.bfill(axis='rows')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhiYLZPBVZNW"
      },
      "source": [
        "# Calculate P/E, P/B and dividend yield using daily closing price\n",
        "processed_full['PE'] = processed_full['close']/processed_full['EPS']\n",
        "processed_full['PB'] = processed_full['close']/processed_full['BPS']\n",
        "processed_full['Div_yield'] = processed_full['DPS']/processed_full['close']\n",
        "processed_full = processed_full.drop(columns=['day','EPS','BPS','DPS'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "grvhGJJII3Xn",
        "outputId": "e2576767-2e94-44e2-cc11-b605f5daafa3"
      },
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>0.217886</td>\n",
              "      <td>0.163846</td>\n",
              "      <td>0.103222</td>\n",
              "      <td>0.183579</td>\n",
              "      <td>2.461857</td>\n",
              "      <td>2.039779</td>\n",
              "      <td>1.818995</td>\n",
              "      <td>54.403846</td>\n",
              "      <td>8.972003</td>\n",
              "      <td>4.269115</td>\n",
              "      <td>0.437727</td>\n",
              "      <td>0.778495</td>\n",
              "      <td>0.640691</td>\n",
              "      <td>0.102249</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>0.093973</td>\n",
              "      <td>0.072040</td>\n",
              "      <td>0.014094</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.351354</td>\n",
              "      <td>0.653355</td>\n",
              "      <td>0.869784</td>\n",
              "      <td>6.679531</td>\n",
              "      <td>50.507629</td>\n",
              "      <td>1.450032</td>\n",
              "      <td>0.011496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>0.047307</td>\n",
              "      <td>0.032525</td>\n",
              "      <td>0.026400</td>\n",
              "      <td>-2.870334</td>\n",
              "      <td>0.927883</td>\n",
              "      <td>0.368463</td>\n",
              "      <td>0.148507</td>\n",
              "      <td>2.329670</td>\n",
              "      <td>6.815203</td>\n",
              "      <td>2.076967</td>\n",
              "      <td>1.009198</td>\n",
              "      <td>-109.722986</td>\n",
              "      <td>39.012760</td>\n",
              "      <td>-35.751054</td>\n",
              "      <td>0.012374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>0.124545</td>\n",
              "      <td>0.066662</td>\n",
              "      <td>0.040891</td>\n",
              "      <td>0.415878</td>\n",
              "      <td>1.343293</td>\n",
              "      <td>0.890488</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>3.540791</td>\n",
              "      <td>2.460351</td>\n",
              "      <td>8.472455</td>\n",
              "      <td>0.893715</td>\n",
              "      <td>9.089489</td>\n",
              "      <td>-172.791371</td>\n",
              "      <td>3.168809</td>\n",
              "      <td>0.012793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>0.234698</td>\n",
              "      <td>0.196418</td>\n",
              "      <td>0.097593</td>\n",
              "      <td>0.162793</td>\n",
              "      <td>2.792929</td>\n",
              "      <td>2.498162</td>\n",
              "      <td>2.170759</td>\n",
              "      <td>9.054201</td>\n",
              "      <td>6.844634</td>\n",
              "      <td>16.036800</td>\n",
              "      <td>0.400215</td>\n",
              "      <td>0.667591</td>\n",
              "      <td>19.850408</td>\n",
              "      <td>1.986886</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>46.800758</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>0.141417</td>\n",
              "      <td>0.097223</td>\n",
              "      <td>0.117691</td>\n",
              "      <td>0.213663</td>\n",
              "      <td>1.368819</td>\n",
              "      <td>0.952878</td>\n",
              "      <td>0.373760</td>\n",
              "      <td>23.920348</td>\n",
              "      <td>13.387209</td>\n",
              "      <td>11.276861</td>\n",
              "      <td>0.449174</td>\n",
              "      <td>0.815455</td>\n",
              "      <td>50.870390</td>\n",
              "      <td>1.074527</td>\n",
              "      <td>0.013889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DD</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.281582</td>\n",
              "      <td>13251037.0</td>\n",
              "      <td>0.167221</td>\n",
              "      <td>0.102157</td>\n",
              "      <td>0.045834</td>\n",
              "      <td>0.084230</td>\n",
              "      <td>1.175830</td>\n",
              "      <td>0.815629</td>\n",
              "      <td>0.330748</td>\n",
              "      <td>11.310223</td>\n",
              "      <td>5.725855</td>\n",
              "      <td>4.287167</td>\n",
              "      <td>0.455848</td>\n",
              "      <td>0.837721</td>\n",
              "      <td>19.343775</td>\n",
              "      <td>0.835775</td>\n",
              "      <td>0.022903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DIS</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>0.167221</td>\n",
              "      <td>0.102157</td>\n",
              "      <td>0.045834</td>\n",
              "      <td>0.084230</td>\n",
              "      <td>1.175830</td>\n",
              "      <td>0.815629</td>\n",
              "      <td>0.330748</td>\n",
              "      <td>11.310223</td>\n",
              "      <td>5.725855</td>\n",
              "      <td>4.287167</td>\n",
              "      <td>0.455848</td>\n",
              "      <td>0.837721</td>\n",
              "      <td>26.072780</td>\n",
              "      <td>1.126511</td>\n",
              "      <td>0.016992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.315208</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>0.608183</td>\n",
              "      <td>0.023205</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.012761</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.036288</td>\n",
              "      <td>0.116995</td>\n",
              "      <td>0.044756</td>\n",
              "      <td>0.930155</td>\n",
              "      <td>13.542445</td>\n",
              "      <td>20.780232</td>\n",
              "      <td>0.731821</td>\n",
              "      <td>0.006454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>HD</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.705078</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>0.082162</td>\n",
              "      <td>0.040825</td>\n",
              "      <td>0.056214</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.198063</td>\n",
              "      <td>0.134224</td>\n",
              "      <td>0.047073</td>\n",
              "      <td>3.526750</td>\n",
              "      <td>58.313786</td>\n",
              "      <td>7.806097</td>\n",
              "      <td>0.568142</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.212745</td>\n",
              "      <td>1.689138</td>\n",
              "      <td>0.012708</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic       open  ...          PE         PB  Div_yield\n",
              "0 2009-01-02  AAPL   3.067143  ...    0.640691   0.102249   0.000000\n",
              "1 2009-01-02   AXP  18.570000  ...   50.507629   1.450032   0.011496\n",
              "2 2009-01-02    BA  42.799999  ...   39.012760 -35.751054   0.012374\n",
              "3 2009-01-02   CAT  44.910000  ... -172.791371   3.168809   0.012793\n",
              "4 2009-01-02  CSCO  16.410000  ...   19.850408   1.986886   0.000000\n",
              "5 2009-01-02   CVX  74.230003  ...   50.870390   1.074527   0.013889\n",
              "6 2009-01-02    DD  21.605234  ...   19.343775   0.835775   0.022903\n",
              "7 2009-01-02   DIS  22.760000  ...   26.072780   1.126511   0.016992\n",
              "8 2009-01-02    GS  84.019997  ...   20.780232   0.731821   0.006454\n",
              "9 2009-01-02    HD  23.070000  ...   13.212745   1.689138   0.012708\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "fc0bccfb-0f90-4cd8-b70d-1631e689e043"
      },
      "source": [
        "train = data_split(processed_full, '2009-01-01','2019-01-01')\n",
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "print(len(train))\n",
        "print(len(trade))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "109530\n",
            "21930\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "p52zNCOhTtLR",
        "outputId": "ae23e3a5-1d7e-4ab9-9a34-9e7346698af4"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>date</th>\n",
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              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>0.217886</td>\n",
              "      <td>0.163846</td>\n",
              "      <td>0.103222</td>\n",
              "      <td>0.183579</td>\n",
              "      <td>2.461857</td>\n",
              "      <td>2.039779</td>\n",
              "      <td>1.818995</td>\n",
              "      <td>54.403846</td>\n",
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              "      <td>0.102249</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>0.093973</td>\n",
              "      <td>0.072040</td>\n",
              "      <td>0.014094</td>\n",
              "      <td>0.108238</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.351354</td>\n",
              "      <td>0.653355</td>\n",
              "      <td>0.869784</td>\n",
              "      <td>6.679531</td>\n",
              "      <td>50.507629</td>\n",
              "      <td>1.450032</td>\n",
              "      <td>0.011496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>0.047307</td>\n",
              "      <td>0.032525</td>\n",
              "      <td>0.026400</td>\n",
              "      <td>-2.870334</td>\n",
              "      <td>0.927883</td>\n",
              "      <td>0.368463</td>\n",
              "      <td>0.148507</td>\n",
              "      <td>2.329670</td>\n",
              "      <td>6.815203</td>\n",
              "      <td>2.076967</td>\n",
              "      <td>1.009198</td>\n",
              "      <td>-109.722986</td>\n",
              "      <td>39.012760</td>\n",
              "      <td>-35.751054</td>\n",
              "      <td>0.012374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>0.124545</td>\n",
              "      <td>0.066662</td>\n",
              "      <td>0.040891</td>\n",
              "      <td>0.415878</td>\n",
              "      <td>1.343293</td>\n",
              "      <td>0.890488</td>\n",
              "      <td>0.163158</td>\n",
              "      <td>3.540791</td>\n",
              "      <td>2.460351</td>\n",
              "      <td>8.472455</td>\n",
              "      <td>0.893715</td>\n",
              "      <td>9.089489</td>\n",
              "      <td>-172.791371</td>\n",
              "      <td>3.168809</td>\n",
              "      <td>0.012793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>0.234698</td>\n",
              "      <td>0.196418</td>\n",
              "      <td>0.097593</td>\n",
              "      <td>0.162793</td>\n",
              "      <td>2.792929</td>\n",
              "      <td>2.498162</td>\n",
              "      <td>2.170759</td>\n",
              "      <td>9.054201</td>\n",
              "      <td>6.844634</td>\n",
              "      <td>16.036800</td>\n",
              "      <td>0.400215</td>\n",
              "      <td>0.667591</td>\n",
              "      <td>19.850408</td>\n",
              "      <td>1.986886</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic       open  ...          PE         PB  Div_yield\n",
              "0 2009-01-02  AAPL   3.067143  ...    0.640691   0.102249   0.000000\n",
              "0 2009-01-02   AXP  18.570000  ...   50.507629   1.450032   0.011496\n",
              "0 2009-01-02    BA  42.799999  ...   39.012760 -35.751054   0.012374\n",
              "0 2009-01-02   CAT  44.910000  ... -172.791371   3.168809   0.012793\n",
              "0 2009-01-02  CSCO  16.410000  ...   19.850408   1.986886   0.000000\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "ae8bf9b1-b42b-480f-c0ea-06c65f0d1153"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.439735</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>0.258891</td>\n",
              "      <td>0.227773</td>\n",
              "      <td>0.133360</td>\n",
              "      <td>0.430843</td>\n",
              "      <td>1.315382</td>\n",
              "      <td>1.134347</td>\n",
              "      <td>0.854114</td>\n",
              "      <td>23.571867</td>\n",
              "      <td>7.620024</td>\n",
              "      <td>3.781658</td>\n",
              "      <td>0.690466</td>\n",
              "      <td>2.230663</td>\n",
              "      <td>5.737274</td>\n",
              "      <td>1.672991</td>\n",
              "      <td>0.018991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>91.803406</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>0.203479</td>\n",
              "      <td>0.160494</td>\n",
              "      <td>0.026811</td>\n",
              "      <td>0.237960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.231669</td>\n",
              "      <td>0.279424</td>\n",
              "      <td>0.887329</td>\n",
              "      <td>7.875371</td>\n",
              "      <td>50.720114</td>\n",
              "      <td>3.458432</td>\n",
              "      <td>0.004248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>0.116496</td>\n",
              "      <td>0.102682</td>\n",
              "      <td>0.066409</td>\n",
              "      <td>34.409483</td>\n",
              "      <td>1.070490</td>\n",
              "      <td>0.262465</td>\n",
              "      <td>0.092436</td>\n",
              "      <td>0.933164</td>\n",
              "      <td>5.468453</td>\n",
              "      <td>4.151637</td>\n",
              "      <td>0.998070</td>\n",
              "      <td>517.142241</td>\n",
              "      <td>83.019826</td>\n",
              "      <td>1418.196271</td>\n",
              "      <td>0.006531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>118.137177</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>0.186871</td>\n",
              "      <td>0.107064</td>\n",
              "      <td>0.056932</td>\n",
              "      <td>0.289572</td>\n",
              "      <td>1.428582</td>\n",
              "      <td>0.919490</td>\n",
              "      <td>0.266175</td>\n",
              "      <td>2.135008</td>\n",
              "      <td>2.339630</td>\n",
              "      <td>3.660183</td>\n",
              "      <td>0.803394</td>\n",
              "      <td>4.086316</td>\n",
              "      <td>35.907956</td>\n",
              "      <td>4.375155</td>\n",
              "      <td>0.007280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>39.496738</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>0.263373</td>\n",
              "      <td>0.261680</td>\n",
              "      <td>0.098017</td>\n",
              "      <td>0.246218</td>\n",
              "      <td>1.801859</td>\n",
              "      <td>1.677431</td>\n",
              "      <td>1.370671</td>\n",
              "      <td>7.722516</td>\n",
              "      <td>4.244056</td>\n",
              "      <td>7.937160</td>\n",
              "      <td>0.601911</td>\n",
              "      <td>1.512001</td>\n",
              "      <td>28.011871</td>\n",
              "      <td>4.283841</td>\n",
              "      <td>0.008355</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic        open  ...         PE           PB  Div_yield\n",
              "0 2019-01-01  AAPL   38.722500  ...   5.737274     1.672991   0.018991\n",
              "0 2019-01-01   AXP   93.910004  ...  50.720114     3.458432   0.004248\n",
              "0 2019-01-01    BA  316.190002  ...  83.019826  1418.196271   0.006531\n",
              "0 2019-01-01   CAT  124.029999  ...  35.907956     4.375155   0.007280\n",
              "0 2019-01-01  CSCO   42.279999  ...  28.011871     4.283841   0.008355\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYN573SOHhxG"
      },
      "source": [
        "ratio_list = ['OPM', 'NPM','ROA', 'ROE', 'cur_ratio', 'quick_ratio', 'cash_ratio', 'inv_turnover','acc_rec_turnover', 'acc_pay_turnover', 'debt_ratio', 'debt_to_equity',\n",
        "       'PE', 'PB', 'Div_yield']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "8e49c82f-2e47-4794-d731-1967c8c92cc2"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(ratio_list)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001,\n",
        "    \"sell_cost_pct\": 0.001,\n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": ratio_list, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4\n",
        "    \n",
        "}\n",
        "\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "7c3b5bd5-a3d8-4e8f-cfd6-bdaaaeff55a3"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "74cb8fcc-f643-442a-d592-0704db70dc5f"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "0cd8f84e-5665-4be9-fb1a-344d4a4f1ce3"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=100000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 60       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 8        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.157    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 18.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 68       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.0513   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 51.6     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 17.2     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 71        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 20        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.9     |\n",
            "|    explained_variance | -1.19e-06 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -1.03     |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 0.541     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 73       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 27       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 103      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.78     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 74       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 33       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 158      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 23.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 74       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 40       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 62.7     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -136     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 17.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 1.91e+06 |\n",
            "|    total_cost         | 7.56e+04 |\n",
            "|    total_reward       | 9.14e+05 |\n",
            "|    total_reward_pct   | 91.4     |\n",
            "|    total_trades       | 73316    |\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | -50.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.55     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 59       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 76.5     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 175      |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 17.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 72       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -14.7    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 0.158    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 78       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -84.2    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.71     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 1300      |\n",
            "|    time_elapsed       | 84        |\n",
            "|    total_timesteps    | 6500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.3     |\n",
            "|    explained_variance | -4.77e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1299      |\n",
            "|    policy_loss        | -63.5     |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 3.31      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | -309     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 60.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.29e+06 |\n",
            "|    total_cost         | 2.63e+04 |\n",
            "|    total_reward       | 1.29e+06 |\n",
            "|    total_reward_pct   | 129      |\n",
            "|    total_trades       | 60684    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | -0.33    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | 61.1     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.06     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | -19.4    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.955    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 110       |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | -50.8     |\n",
            "|    std                | 1.03      |\n",
            "|    value_loss         | 2.76      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 116      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 19.4     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.647    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 123      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -187     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 22.8     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 129       |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.5     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | -71.4     |\n",
            "|    std                | 1.03      |\n",
            "|    value_loss         | 4.75      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 135      |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 73.2     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 4.46     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.38e+06 |\n",
            "|    total_cost         | 3.17e+04 |\n",
            "|    total_reward       | 3.38e+06 |\n",
            "|    total_reward_pct   | 338      |\n",
            "|    total_trades       | 65214    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | -37.4    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 148      |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | -52.1    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 2.56     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 155      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | -91.9    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 6.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 161      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 19.6     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.766    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 167      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | -58.4    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 2.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 174      |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | -177     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 24.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 180      |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 54.1     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 1.96     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 187      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 15.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 2.92e+06 |\n",
            "|    total_cost         | 1.85e+04 |\n",
            "|    total_reward       | 1.92e+06 |\n",
            "|    total_reward_pct   | 192      |\n",
            "|    total_trades       | 67453    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 193      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | -97.9    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 5.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 199      |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | -87.5    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 206      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | -199     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 21.3     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3300      |\n",
            "|    time_elapsed       | 212       |\n",
            "|    total_timesteps    | 16500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44       |\n",
            "|    explained_variance | -3.58e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3299      |\n",
            "|    policy_loss        | 59.4      |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 4.14      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 219      |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | -32.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.65     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 225      |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | -85.8    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.92     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3600      |\n",
            "|    time_elapsed       | 231       |\n",
            "|    total_timesteps    | 18000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3599      |\n",
            "|    policy_loss        | 118       |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 14.3      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.75e+06 |\n",
            "|    total_cost         | 2.33e+04 |\n",
            "|    total_reward       | 2.75e+06 |\n",
            "|    total_reward_pct   | 275      |\n",
            "|    total_trades       | 65849    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 238      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | -44.2    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.43     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 244       |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 59.3      |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 1.79      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 251      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 82.2     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 3.91     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 257      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | -153     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 13.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 88       |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 4.7      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 270      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | -0.0553  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | -65      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 5.44     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 276      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | -55.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 2.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.83e+06 |\n",
            "|    total_cost         | 8.83e+04 |\n",
            "|    total_reward       | 2.83e+06 |\n",
            "|    total_reward_pct   | 283      |\n",
            "|    total_trades       | 71634    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | -0.0125  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 94.9     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 5.92     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 289      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | -95.7    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 6.98     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 296      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 0.903    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 302      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | -79.2    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 2.97     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 308      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | -23.5    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.842    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 315      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 54.6     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 7.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 321      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 34.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.99     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 327      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 352      |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 72.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.24e+06 |\n",
            "|    total_cost         | 1.66e+04 |\n",
            "|    total_reward       | 2.24e+06 |\n",
            "|    total_reward_pct   | 224      |\n",
            "|    total_trades       | 61666    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 334      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 2.88     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.137    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 340      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | -222     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 30.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 347      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -11.7    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.156    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 353      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 13.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 359      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 15.9     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 5700      |\n",
            "|    time_elapsed       | 366       |\n",
            "|    total_timesteps    | 28500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 5699      |\n",
            "|    policy_loss        | 26.7      |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.48      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 372      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 242      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 37       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.16e+06 |\n",
            "|    total_cost         | 1.22e+04 |\n",
            "|    total_reward       | 2.16e+06 |\n",
            "|    total_reward_pct   | 216      |\n",
            "|    total_trades       | 60801    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 379      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 72.7     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.37     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 385      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 58       |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.2      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 392      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | -132     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 9.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 398      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | 25.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.75     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 6300      |\n",
            "|    time_elapsed       | 404       |\n",
            "|    total_timesteps    | 31500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6299      |\n",
            "|    policy_loss        | -84.4     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 5.68      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 411      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | -74.6    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.73     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 417      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 120      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 21.8     |\n",
            "------------------------------------\n",
            "day: 3650, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4297821.94\n",
            "total_reward: 3297821.94\n",
            "total_cost: 12437.82\n",
            "total_trades: 58594\n",
            "Sharpe: 0.695\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.3e+06  |\n",
            "|    total_cost         | 1.24e+04 |\n",
            "|    total_reward       | 3.3e+06  |\n",
            "|    total_reward_pct   | 330      |\n",
            "|    total_trades       | 58594    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 424      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | -0.112   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | 63.4     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 4.21     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 430      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 31.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.92     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 6800      |\n",
            "|    time_elapsed       | 437       |\n",
            "|    total_timesteps    | 34000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6799      |\n",
            "|    policy_loss        | 141       |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 16.5      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 443      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | -198     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 23.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 450      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 132      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 12.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 456      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | -99.9    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 5.45     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 7200      |\n",
            "|    time_elapsed       | 462       |\n",
            "|    total_timesteps    | 36000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7199      |\n",
            "|    policy_loss        | 210       |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 32.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 7300      |\n",
            "|    time_elapsed       | 469       |\n",
            "|    total_timesteps    | 36500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7299      |\n",
            "|    policy_loss        | -476      |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 271       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.94e+06 |\n",
            "|    total_cost         | 8.73e+03 |\n",
            "|    total_reward       | 3.94e+06 |\n",
            "|    total_reward_pct   | 394      |\n",
            "|    total_trades       | 58374    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 475      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 42.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.51     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 482      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 140      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 10.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 488      |\n",
            "|    total_timesteps    | 38000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7599     |\n",
            "|    policy_loss        | -201     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 45.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 495      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | 28.5     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 501      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 520      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 162      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 508      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7899     |\n",
            "|    policy_loss        | -54.3    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 514      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 58.2     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.26     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.63e+06  |\n",
            "|    total_cost         | 8.6e+03   |\n",
            "|    total_reward       | 3.63e+06  |\n",
            "|    total_reward_pct   | 363       |\n",
            "|    total_trades       | 58469     |\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 8100      |\n",
            "|    time_elapsed       | 520       |\n",
            "|    total_timesteps    | 40500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8099      |\n",
            "|    policy_loss        | -20.9     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.355     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 527      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | -38.6    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.935    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 533      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | 11.9     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.774    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 540      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | -39.8    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.31     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 546      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | -128     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 16.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 553      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | 70.4     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 559      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 170      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 57.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.48e+06 |\n",
            "|    total_cost         | 5.81e+03 |\n",
            "|    total_reward       | 3.48e+06 |\n",
            "|    total_reward_pct   | 348      |\n",
            "|    total_trades       | 57804    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 565      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0.00216  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | -63      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.26     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 8900     |\n",
            "|    time_elapsed       | 572      |\n",
            "|    total_timesteps    | 44500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8899     |\n",
            "|    policy_loss        | -111     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 7.1      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 579      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | -72.9    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.48     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 585       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | -18.6     |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.765     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 592      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 71.5     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 598      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | -8.18    |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 0.324    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 605      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | -262     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 34.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.96e+06 |\n",
            "|    total_cost         | 4.92e+03 |\n",
            "|    total_reward       | 2.96e+06 |\n",
            "|    total_reward_pct   | 296      |\n",
            "|    total_trades       | 58228    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 611      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0.111    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 58.7     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 2.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 618      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 103      |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 7.2      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 624       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | -62       |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 4.3       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 631      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 25.5     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 9900      |\n",
            "|    time_elapsed       | 637       |\n",
            "|    total_timesteps    | 49500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9899      |\n",
            "|    policy_loss        | 34.8      |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 1.79      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 644      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | -288     |\n",
            "|    std                | 1.07     |\n",
            "|    value_loss         | 45.4     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 10100     |\n",
            "|    time_elapsed       | 650       |\n",
            "|    total_timesteps    | 50500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 10099     |\n",
            "|    policy_loss        | 176       |\n",
            "|    std                | 1.07      |\n",
            "|    value_loss         | 17.8      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10200    |\n",
            "|    time_elapsed       | 657      |\n",
            "|    total_timesteps    | 51000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10199    |\n",
            "|    policy_loss        | -22.3    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.66     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.38e+06 |\n",
            "|    total_cost         | 5.66e+03 |\n",
            "|    total_reward       | 3.38e+06 |\n",
            "|    total_reward_pct   | 338      |\n",
            "|    total_trades       | 61155    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10300    |\n",
            "|    time_elapsed       | 664      |\n",
            "|    total_timesteps    | 51500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 3.46e-06 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10299    |\n",
            "|    policy_loss        | -18.2    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10400    |\n",
            "|    time_elapsed       | 670      |\n",
            "|    total_timesteps    | 52000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10399    |\n",
            "|    policy_loss        | -166     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 15.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10500    |\n",
            "|    time_elapsed       | 677      |\n",
            "|    total_timesteps    | 52500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10499    |\n",
            "|    policy_loss        | -1.79    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.733    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10600    |\n",
            "|    time_elapsed       | 683      |\n",
            "|    total_timesteps    | 53000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10599    |\n",
            "|    policy_loss        | -46.4    |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 1.3      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10700    |\n",
            "|    time_elapsed       | 690      |\n",
            "|    total_timesteps    | 53500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10699    |\n",
            "|    policy_loss        | 358      |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 72.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10800    |\n",
            "|    time_elapsed       | 696      |\n",
            "|    total_timesteps    | 54000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10799    |\n",
            "|    policy_loss        | 26.8     |\n",
            "|    std                | 1.08     |\n",
            "|    value_loss         | 0.888    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 10900    |\n",
            "|    time_elapsed       | 703      |\n",
            "|    total_timesteps    | 54500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10899    |\n",
            "|    policy_loss        | -124     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 34.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.93e+06 |\n",
            "|    total_cost         | 7.95e+03 |\n",
            "|    total_reward       | 2.93e+06 |\n",
            "|    total_reward_pct   | 293      |\n",
            "|    total_trades       | 62120    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11000    |\n",
            "|    time_elapsed       | 710      |\n",
            "|    total_timesteps    | 55000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 10999    |\n",
            "|    policy_loss        | 88.9     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 4.3      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11100    |\n",
            "|    time_elapsed       | 716      |\n",
            "|    total_timesteps    | 55500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11099    |\n",
            "|    policy_loss        | 25.5     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 0.656    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11200    |\n",
            "|    time_elapsed       | 723      |\n",
            "|    total_timesteps    | 56000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11199    |\n",
            "|    policy_loss        | -91.8    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 5        |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11300    |\n",
            "|    time_elapsed       | 729      |\n",
            "|    total_timesteps    | 56500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11299    |\n",
            "|    policy_loss        | -168     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 18       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11400    |\n",
            "|    time_elapsed       | 736      |\n",
            "|    total_timesteps    | 57000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11399    |\n",
            "|    policy_loss        | 163      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 11.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11500    |\n",
            "|    time_elapsed       | 742      |\n",
            "|    total_timesteps    | 57500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11499    |\n",
            "|    policy_loss        | -269     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 35.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11600    |\n",
            "|    time_elapsed       | 749      |\n",
            "|    total_timesteps    | 58000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11599    |\n",
            "|    policy_loss        | 117      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 19.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.86e+06 |\n",
            "|    total_cost         | 9.73e+03 |\n",
            "|    total_reward       | 2.86e+06 |\n",
            "|    total_reward_pct   | 286      |\n",
            "|    total_trades       | 59593    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11700    |\n",
            "|    time_elapsed       | 756      |\n",
            "|    total_timesteps    | 58500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11699    |\n",
            "|    policy_loss        | 146      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 15       |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 11800     |\n",
            "|    time_elapsed       | 762       |\n",
            "|    total_timesteps    | 59000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.1     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 11799     |\n",
            "|    policy_loss        | -6.42     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 0.452     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 11900    |\n",
            "|    time_elapsed       | 769      |\n",
            "|    total_timesteps    | 59500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11899    |\n",
            "|    policy_loss        | -116     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 8.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12000    |\n",
            "|    time_elapsed       | 775      |\n",
            "|    total_timesteps    | 60000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 11999    |\n",
            "|    policy_loss        | 115      |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 7.46     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12100    |\n",
            "|    time_elapsed       | 782      |\n",
            "|    total_timesteps    | 60500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12099    |\n",
            "|    policy_loss        | 27.9     |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 2.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12200    |\n",
            "|    time_elapsed       | 788      |\n",
            "|    total_timesteps    | 61000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12199    |\n",
            "|    policy_loss        | -49.7    |\n",
            "|    std                | 1.09     |\n",
            "|    value_loss         | 4.56     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12300     |\n",
            "|    time_elapsed       | 795       |\n",
            "|    total_timesteps    | 61500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12299     |\n",
            "|    policy_loss        | -61.2     |\n",
            "|    std                | 1.09      |\n",
            "|    value_loss         | 2.91      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12400    |\n",
            "|    time_elapsed       | 801      |\n",
            "|    total_timesteps    | 62000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12399    |\n",
            "|    policy_loss        | 74.6     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 15.6     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.12e+06  |\n",
            "|    total_cost         | 5.9e+03   |\n",
            "|    total_reward       | 3.12e+06  |\n",
            "|    total_reward_pct   | 312       |\n",
            "|    total_trades       | 61004     |\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12500     |\n",
            "|    time_elapsed       | 808       |\n",
            "|    total_timesteps    | 62500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12499     |\n",
            "|    policy_loss        | 61        |\n",
            "|    std                | 1.1       |\n",
            "|    value_loss         | 3.23      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12600    |\n",
            "|    time_elapsed       | 815      |\n",
            "|    total_timesteps    | 63000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12599    |\n",
            "|    policy_loss        | 74.9     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 2.61     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12700    |\n",
            "|    time_elapsed       | 821      |\n",
            "|    total_timesteps    | 63500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12699    |\n",
            "|    policy_loss        | 77.4     |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 3.81     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 12800    |\n",
            "|    time_elapsed       | 828      |\n",
            "|    total_timesteps    | 64000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12799    |\n",
            "|    policy_loss        | -14.7    |\n",
            "|    std                | 1.1      |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 12900     |\n",
            "|    time_elapsed       | 834       |\n",
            "|    total_timesteps    | 64500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.5     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 12899     |\n",
            "|    policy_loss        | 1.03e+03  |\n",
            "|    std                | 1.1       |\n",
            "|    value_loss         | 505       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13000    |\n",
            "|    time_elapsed       | 841      |\n",
            "|    total_timesteps    | 65000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 12999    |\n",
            "|    policy_loss        | 124      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 11.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13100    |\n",
            "|    time_elapsed       | 848      |\n",
            "|    total_timesteps    | 65500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13099    |\n",
            "|    policy_loss        | -15      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 1.22     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.65e+06 |\n",
            "|    total_cost         | 8.07e+03 |\n",
            "|    total_reward       | 3.65e+06 |\n",
            "|    total_reward_pct   | 365      |\n",
            "|    total_trades       | 62460    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13200    |\n",
            "|    time_elapsed       | 854      |\n",
            "|    total_timesteps    | 66000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13199    |\n",
            "|    policy_loss        | 46.3     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.915    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13300    |\n",
            "|    time_elapsed       | 861      |\n",
            "|    total_timesteps    | 66500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13299    |\n",
            "|    policy_loss        | -14.1    |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.13     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13400    |\n",
            "|    time_elapsed       | 867      |\n",
            "|    total_timesteps    | 67000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13399    |\n",
            "|    policy_loss        | 65       |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 4.92     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13500    |\n",
            "|    time_elapsed       | 874      |\n",
            "|    total_timesteps    | 67500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13499    |\n",
            "|    policy_loss        | 121      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 7.18     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 13600     |\n",
            "|    time_elapsed       | 880       |\n",
            "|    total_timesteps    | 68000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 13599     |\n",
            "|    policy_loss        | 81.3      |\n",
            "|    std                | 1.11      |\n",
            "|    value_loss         | 14.1      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13700    |\n",
            "|    time_elapsed       | 887      |\n",
            "|    total_timesteps    | 68500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13699    |\n",
            "|    policy_loss        | 104      |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 5.04     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13800    |\n",
            "|    time_elapsed       | 893      |\n",
            "|    total_timesteps    | 69000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13799    |\n",
            "|    policy_loss        | -263     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 31.9     |\n",
            "------------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3959377.31\n",
            "total_reward: 2959377.31\n",
            "total_cost: 5535.60\n",
            "total_trades: 64004\n",
            "Sharpe: 0.755\n",
            "=================================\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.96e+06 |\n",
            "|    total_cost         | 5.54e+03 |\n",
            "|    total_reward       | 2.96e+06 |\n",
            "|    total_reward_pct   | 296      |\n",
            "|    total_trades       | 64004    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 13900    |\n",
            "|    time_elapsed       | 900      |\n",
            "|    total_timesteps    | 69500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13899    |\n",
            "|    policy_loss        | 25.9     |\n",
            "|    std                | 1.11     |\n",
            "|    value_loss         | 0.428    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14000    |\n",
            "|    time_elapsed       | 907      |\n",
            "|    total_timesteps    | 70000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 13999    |\n",
            "|    policy_loss        | 40.9     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14100    |\n",
            "|    time_elapsed       | 913      |\n",
            "|    total_timesteps    | 70500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14099    |\n",
            "|    policy_loss        | -0.995   |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.024    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14200    |\n",
            "|    time_elapsed       | 920      |\n",
            "|    total_timesteps    | 71000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14199    |\n",
            "|    policy_loss        | 21.5     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.753    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14300    |\n",
            "|    time_elapsed       | 926      |\n",
            "|    total_timesteps    | 71500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14299    |\n",
            "|    policy_loss        | 96.7     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.54     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14400    |\n",
            "|    time_elapsed       | 933      |\n",
            "|    total_timesteps    | 72000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14399    |\n",
            "|    policy_loss        | 99.8     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.77     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14500    |\n",
            "|    time_elapsed       | 940      |\n",
            "|    total_timesteps    | 72500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14499    |\n",
            "|    policy_loss        | 43.6     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.32     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14600    |\n",
            "|    time_elapsed       | 946      |\n",
            "|    total_timesteps    | 73000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14599    |\n",
            "|    policy_loss        | -874     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 365      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 3.87e+06 |\n",
            "|    total_cost         | 5.34e+03 |\n",
            "|    total_reward       | 2.87e+06 |\n",
            "|    total_reward_pct   | 287      |\n",
            "|    total_trades       | 67475    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14700    |\n",
            "|    time_elapsed       | 953      |\n",
            "|    total_timesteps    | 73500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14699    |\n",
            "|    policy_loss        | 36.2     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.616    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14800    |\n",
            "|    time_elapsed       | 960      |\n",
            "|    total_timesteps    | 74000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14799    |\n",
            "|    policy_loss        | -129     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 12.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 14900    |\n",
            "|    time_elapsed       | 966      |\n",
            "|    total_timesteps    | 74500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14899    |\n",
            "|    policy_loss        | 27.7     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 1.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15000    |\n",
            "|    time_elapsed       | 973      |\n",
            "|    total_timesteps    | 75000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 14999    |\n",
            "|    policy_loss        | 84       |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 5.1      |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15100     |\n",
            "|    time_elapsed       | 979       |\n",
            "|    total_timesteps    | 75500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -45.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15099     |\n",
            "|    policy_loss        | -7.35     |\n",
            "|    std                | 1.12      |\n",
            "|    value_loss         | 1.71      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15200    |\n",
            "|    time_elapsed       | 986      |\n",
            "|    total_timesteps    | 76000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15199    |\n",
            "|    policy_loss        | 126      |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 8.75     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15300    |\n",
            "|    time_elapsed       | 993      |\n",
            "|    total_timesteps    | 76500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -45.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15299    |\n",
            "|    policy_loss        | 190      |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 31.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.14e+06 |\n",
            "|    total_cost         | 4.28e+03 |\n",
            "|    total_reward       | 3.14e+06 |\n",
            "|    total_reward_pct   | 314      |\n",
            "|    total_trades       | 66224    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15400    |\n",
            "|    time_elapsed       | 999      |\n",
            "|    total_timesteps    | 77000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15399    |\n",
            "|    policy_loss        | 15.4     |\n",
            "|    std                | 1.12     |\n",
            "|    value_loss         | 0.418    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15500     |\n",
            "|    time_elapsed       | 1006      |\n",
            "|    total_timesteps    | 77500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.1     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15499     |\n",
            "|    policy_loss        | -1.22     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 0.0144    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 15600    |\n",
            "|    time_elapsed       | 1012     |\n",
            "|    total_timesteps    | 78000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15599    |\n",
            "|    policy_loss        | 126      |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 6.33     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 15700     |\n",
            "|    time_elapsed       | 1019      |\n",
            "|    total_timesteps    | 78500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15699     |\n",
            "|    policy_loss        | -1.57     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 0.0992    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 15800    |\n",
            "|    time_elapsed       | 1026     |\n",
            "|    total_timesteps    | 79000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.2    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15799    |\n",
            "|    policy_loss        | 177      |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 15.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 15900    |\n",
            "|    time_elapsed       | 1032     |\n",
            "|    total_timesteps    | 79500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 15899    |\n",
            "|    policy_loss        | -99.7    |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16000     |\n",
            "|    time_elapsed       | 1039      |\n",
            "|    total_timesteps    | 80000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 15999     |\n",
            "|    policy_loss        | -482      |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 114       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 3.94e+06  |\n",
            "|    total_cost         | 3.49e+03  |\n",
            "|    total_reward       | 2.94e+06  |\n",
            "|    total_reward_pct   | 294       |\n",
            "|    total_trades       | 64301     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16100     |\n",
            "|    time_elapsed       | 1045      |\n",
            "|    total_timesteps    | 80500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16099     |\n",
            "|    policy_loss        | -23.5     |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 1.91      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16200    |\n",
            "|    time_elapsed       | 1052     |\n",
            "|    total_timesteps    | 81000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16199    |\n",
            "|    policy_loss        | 46.5     |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 3.81     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16300    |\n",
            "|    time_elapsed       | 1058     |\n",
            "|    total_timesteps    | 81500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16299    |\n",
            "|    policy_loss        | 18.1     |\n",
            "|    std                | 1.13     |\n",
            "|    value_loss         | 0.592    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16400    |\n",
            "|    time_elapsed       | 1065     |\n",
            "|    total_timesteps    | 82000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16399    |\n",
            "|    policy_loss        | 151      |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 11.9     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 16500     |\n",
            "|    time_elapsed       | 1071      |\n",
            "|    total_timesteps    | 82500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16499     |\n",
            "|    policy_loss        | -151      |\n",
            "|    std                | 1.13      |\n",
            "|    value_loss         | 18.1      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 16600    |\n",
            "|    time_elapsed       | 1078     |\n",
            "|    total_timesteps    | 83000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16599    |\n",
            "|    policy_loss        | -409     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 79       |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 77        |\n",
            "|    iterations         | 16700     |\n",
            "|    time_elapsed       | 1084      |\n",
            "|    total_timesteps    | 83500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.3     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 16699     |\n",
            "|    policy_loss        | 44.8      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 7.51      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.12e+06 |\n",
            "|    total_cost         | 3.41e+03 |\n",
            "|    total_reward       | 3.12e+06 |\n",
            "|    total_reward_pct   | 312      |\n",
            "|    total_trades       | 61475    |\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 16800    |\n",
            "|    time_elapsed       | 1090     |\n",
            "|    total_timesteps    | 84000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16799    |\n",
            "|    policy_loss        | 11       |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.286    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 16900    |\n",
            "|    time_elapsed       | 1097     |\n",
            "|    total_timesteps    | 84500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16899    |\n",
            "|    policy_loss        | -24.2    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 5.16     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17000    |\n",
            "|    time_elapsed       | 1103     |\n",
            "|    total_timesteps    | 85000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 16999    |\n",
            "|    policy_loss        | 38.8     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 2.14     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17100    |\n",
            "|    time_elapsed       | 1110     |\n",
            "|    total_timesteps    | 85500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17099    |\n",
            "|    policy_loss        | 1.28     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.161    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17200    |\n",
            "|    time_elapsed       | 1116     |\n",
            "|    total_timesteps    | 86000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17199    |\n",
            "|    policy_loss        | -175     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 14.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 77       |\n",
            "|    iterations         | 17300    |\n",
            "|    time_elapsed       | 1123     |\n",
            "|    total_timesteps    | 86500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17299    |\n",
            "|    policy_loss        | -126     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 11.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17400    |\n",
            "|    time_elapsed       | 1129     |\n",
            "|    total_timesteps    | 87000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17399    |\n",
            "|    policy_loss        | -48.4    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 1.55     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17500     |\n",
            "|    time_elapsed       | 1136      |\n",
            "|    total_timesteps    | 87500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17499     |\n",
            "|    policy_loss        | 139       |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 10.4      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.17e+06  |\n",
            "|    total_cost         | 6.22e+03  |\n",
            "|    total_reward       | 3.17e+06  |\n",
            "|    total_reward_pct   | 317       |\n",
            "|    total_trades       | 58146     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17600     |\n",
            "|    time_elapsed       | 1142      |\n",
            "|    total_timesteps    | 88000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17599     |\n",
            "|    policy_loss        | 10.5      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 0.0937    |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17700    |\n",
            "|    time_elapsed       | 1149     |\n",
            "|    total_timesteps    | 88500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17699    |\n",
            "|    policy_loss        | -62.6    |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 2.89     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 17800    |\n",
            "|    time_elapsed       | 1156     |\n",
            "|    total_timesteps    | 89000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 17799    |\n",
            "|    policy_loss        | 64.3     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 1.99     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 17900     |\n",
            "|    time_elapsed       | 1162      |\n",
            "|    total_timesteps    | 89500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17899     |\n",
            "|    policy_loss        | 36.1      |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 1.18      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18000     |\n",
            "|    time_elapsed       | 1169      |\n",
            "|    total_timesteps    | 90000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.4     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 17999     |\n",
            "|    policy_loss        | 117       |\n",
            "|    std                | 1.14      |\n",
            "|    value_loss         | 13.6      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18100    |\n",
            "|    time_elapsed       | 1176     |\n",
            "|    total_timesteps    | 90500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18099    |\n",
            "|    policy_loss        | 138      |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 25.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18200    |\n",
            "|    time_elapsed       | 1183     |\n",
            "|    total_timesteps    | 91000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18199    |\n",
            "|    policy_loss        | -109     |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 19.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.4e+06  |\n",
            "|    total_cost         | 8.75e+03 |\n",
            "|    total_reward       | 3.4e+06  |\n",
            "|    total_reward_pct   | 340      |\n",
            "|    total_trades       | 64975    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18300    |\n",
            "|    time_elapsed       | 1189     |\n",
            "|    total_timesteps    | 91500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.5    |\n",
            "|    explained_variance | -0.0014  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18299    |\n",
            "|    policy_loss        | 22       |\n",
            "|    std                | 1.14     |\n",
            "|    value_loss         | 0.829    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18400    |\n",
            "|    time_elapsed       | 1196     |\n",
            "|    total_timesteps    | 92000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18399    |\n",
            "|    policy_loss        | 12.7     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 0.192    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18500    |\n",
            "|    time_elapsed       | 1202     |\n",
            "|    total_timesteps    | 92500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18499    |\n",
            "|    policy_loss        | -80.5    |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 5.62     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18600    |\n",
            "|    time_elapsed       | 1209     |\n",
            "|    total_timesteps    | 93000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18599    |\n",
            "|    policy_loss        | 127      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 8.09     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 18700    |\n",
            "|    time_elapsed       | 1215     |\n",
            "|    total_timesteps    | 93500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18699    |\n",
            "|    policy_loss        | -108     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 17.2     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18800     |\n",
            "|    time_elapsed       | 1222      |\n",
            "|    total_timesteps    | 94000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 18799     |\n",
            "|    policy_loss        | -145      |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 11.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 18900     |\n",
            "|    time_elapsed       | 1229      |\n",
            "|    total_timesteps    | 94500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 18899     |\n",
            "|    policy_loss        | 150       |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 14.4      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| environment/          |          |\n",
            "|    portfolio_value    | 4.16e+06 |\n",
            "|    total_cost         | 7.62e+03 |\n",
            "|    total_reward       | 3.16e+06 |\n",
            "|    total_reward_pct   | 316      |\n",
            "|    total_trades       | 66603    |\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19000    |\n",
            "|    time_elapsed       | 1235     |\n",
            "|    total_timesteps    | 95000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 18999    |\n",
            "|    policy_loss        | 260      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 30.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19100    |\n",
            "|    time_elapsed       | 1242     |\n",
            "|    total_timesteps    | 95500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19099    |\n",
            "|    policy_loss        | 97.7     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 7.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19200    |\n",
            "|    time_elapsed       | 1248     |\n",
            "|    total_timesteps    | 96000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19199    |\n",
            "|    policy_loss        | 53.5     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 1.59     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19300    |\n",
            "|    time_elapsed       | 1255     |\n",
            "|    total_timesteps    | 96500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19299    |\n",
            "|    policy_loss        | 165      |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 15.8     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19400    |\n",
            "|    time_elapsed       | 1262     |\n",
            "|    total_timesteps    | 97000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19399    |\n",
            "|    policy_loss        | 1.48     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 0.168    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19500    |\n",
            "|    time_elapsed       | 1268     |\n",
            "|    total_timesteps    | 97500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19499    |\n",
            "|    policy_loss        | -463     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 104      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19600    |\n",
            "|    time_elapsed       | 1275     |\n",
            "|    total_timesteps    | 98000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19599    |\n",
            "|    policy_loss        | -39.8    |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 1.29     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 19700     |\n",
            "|    time_elapsed       | 1281      |\n",
            "|    total_timesteps    | 98500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 19699     |\n",
            "|    policy_loss        | -1.11e+03 |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 583       |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| environment/          |           |\n",
            "|    portfolio_value    | 4.42e+06  |\n",
            "|    total_cost         | 5.67e+03  |\n",
            "|    total_reward       | 3.42e+06  |\n",
            "|    total_reward_pct   | 342       |\n",
            "|    total_trades       | 57577     |\n",
            "| time/                 |           |\n",
            "|    fps                | 76        |\n",
            "|    iterations         | 19800     |\n",
            "|    time_elapsed       | 1288      |\n",
            "|    total_timesteps    | 99000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -46.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 19799     |\n",
            "|    policy_loss        | -3.03     |\n",
            "|    std                | 1.15      |\n",
            "|    value_loss         | 0.427     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 19900    |\n",
            "|    time_elapsed       | 1295     |\n",
            "|    total_timesteps    | 99500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19899    |\n",
            "|    policy_loss        | -127     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 6.54     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 76       |\n",
            "|    iterations         | 20000    |\n",
            "|    time_elapsed       | 1301     |\n",
            "|    total_timesteps    | 100000   |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -46.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 19999    |\n",
            "|    policy_loss        | 79.1     |\n",
            "|    std                | 1.15     |\n",
            "|    value_loss         | 4.91     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "968a03ff-ffe7-4ff3-d503-7887998d4ab4"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cuda device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tCDa78rqfO_a",
        "outputId": "317b22a2-6c77-41f5-e364-0605224038d0"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_2\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2758094.99\n",
            "total_reward: 1758094.99\n",
            "total_cost: 1087.70\n",
            "total_trades: 47475\n",
            "Sharpe: 0.569\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 69       |\n",
            "|    time_elapsed    | 209      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -17.2    |\n",
            "|    critic_loss     | 49.3     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 10953    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 65       |\n",
            "|    time_elapsed    | 443      |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -7.99    |\n",
            "|    critic_loss     | 2.97     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 25557    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 64       |\n",
            "|    time_elapsed    | 677      |\n",
            "|    total timesteps | 43812    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -6.49    |\n",
            "|    critic_loss     | 2.48     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 40161    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2758094.99\n",
            "total_reward: 1758094.99\n",
            "total_cost: 1087.70\n",
            "total_trades: 47475\n",
            "Sharpe: 0.569\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "d5ba80d5-b33e-4f54-8c70-51f6b003119c"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.01,\n",
        "    \"learning_rate\": 0.00025,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Gt8eIQKYM4G3",
        "outputId": "81bbadeb-1229-490c-a8a5-78d523595a14"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_1\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 76   |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 26   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 54          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018077655 |\n",
            "|    clip_fraction        | 0.231       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.0123     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.1         |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0292     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 7.21        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 81          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.013950874 |\n",
            "|    clip_fraction        | 0.17        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.6       |\n",
            "|    explained_variance   | -0.00143    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.92        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0229     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 13.6        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 4          |\n",
            "|    time_elapsed         | 109        |\n",
            "|    total_timesteps      | 8192       |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.01641549 |\n",
            "|    clip_fraction        | 0.183      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -42.6      |\n",
            "|    explained_variance   | -0.0118    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 6.06       |\n",
            "|    n_updates            | 30         |\n",
            "|    policy_gradient_loss | -0.0282    |\n",
            "|    std                  | 1          |\n",
            "|    value_loss           | 11.9       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 136         |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.025979618 |\n",
            "|    clip_fraction        | 0.241       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.00485    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 13          |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0157     |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 19.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 6           |\n",
            "|    time_elapsed         | 163         |\n",
            "|    total_timesteps      | 12288       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016590398 |\n",
            "|    clip_fraction        | 0.21        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.7       |\n",
            "|    explained_variance   | -0.0153     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.53        |\n",
            "|    n_updates            | 50          |\n",
            "|    policy_gradient_loss | -0.0251     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 11          |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 7           |\n",
            "|    time_elapsed         | 191         |\n",
            "|    total_timesteps      | 14336       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020591479 |\n",
            "|    clip_fraction        | 0.229       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.8       |\n",
            "|    explained_variance   | 0.0132      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.55        |\n",
            "|    n_updates            | 60          |\n",
            "|    policy_gradient_loss | -0.0197     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2689089.43\n",
            "total_reward: 1689089.43\n",
            "total_cost: 380034.51\n",
            "total_trades: 104033\n",
            "Sharpe: 0.638\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 8           |\n",
            "|    time_elapsed         | 218         |\n",
            "|    total_timesteps      | 16384       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020969098 |\n",
            "|    clip_fraction        | 0.268       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -42.9       |\n",
            "|    explained_variance   | -0.000622   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7           |\n",
            "|    n_updates            | 70          |\n",
            "|    policy_gradient_loss | -0.0173     |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 12.7        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 9          |\n",
            "|    time_elapsed         | 246        |\n",
            "|    total_timesteps      | 18432      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02511882 |\n",
            "|    clip_fraction        | 0.28       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43        |\n",
            "|    explained_variance   | -0.0182    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 5.87       |\n",
            "|    n_updates            | 80         |\n",
            "|    policy_gradient_loss | -0.0152    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 11.4       |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 10         |\n",
            "|    time_elapsed         | 273        |\n",
            "|    total_timesteps      | 20480      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02978786 |\n",
            "|    clip_fraction        | 0.276      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.1      |\n",
            "|    explained_variance   | 0.00553    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 7.83       |\n",
            "|    n_updates            | 90         |\n",
            "|    policy_gradient_loss | -0.0169    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 18         |\n",
            "----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 11         |\n",
            "|    time_elapsed         | 301        |\n",
            "|    total_timesteps      | 22528      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02986148 |\n",
            "|    clip_fraction        | 0.263      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.1      |\n",
            "|    explained_variance   | -0.0465    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.94       |\n",
            "|    n_updates            | 100        |\n",
            "|    policy_gradient_loss | -0.0156    |\n",
            "|    std                  | 1.02       |\n",
            "|    value_loss           | 9.13       |\n",
            "----------------------------------------\n",
            "---------------------------------------\n",
            "| time/                   |           |\n",
            "|    fps                  | 74        |\n",
            "|    iterations           | 12        |\n",
            "|    time_elapsed         | 328       |\n",
            "|    total_timesteps      | 24576     |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 0.0287299 |\n",
            "|    clip_fraction        | 0.267     |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -43.2     |\n",
            "|    explained_variance   | -0.00762  |\n",
            "|    learning_rate        | 0.00025   |\n",
            "|    loss                 | 9.6       |\n",
            "|    n_updates            | 110       |\n",
            "|    policy_gradient_loss | -0.0165   |\n",
            "|    std                  | 1.02      |\n",
            "|    value_loss           | 20.9      |\n",
            "---------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 13          |\n",
            "|    time_elapsed         | 355         |\n",
            "|    total_timesteps      | 26624       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.023247771 |\n",
            "|    clip_fraction        | 0.264       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.2       |\n",
            "|    explained_variance   | -0.0257     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 2.04        |\n",
            "|    n_updates            | 120         |\n",
            "|    policy_gradient_loss | -0.0105     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 6.91        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 14          |\n",
            "|    time_elapsed         | 382         |\n",
            "|    total_timesteps      | 28672       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.020957708 |\n",
            "|    clip_fraction        | 0.243       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.3       |\n",
            "|    explained_variance   | -0.00506    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.57        |\n",
            "|    n_updates            | 130         |\n",
            "|    policy_gradient_loss | -0.0166     |\n",
            "|    std                  | 1.02        |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 15          |\n",
            "|    time_elapsed         | 410         |\n",
            "|    total_timesteps      | 30720       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.032833345 |\n",
            "|    clip_fraction        | 0.296       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.4       |\n",
            "|    explained_variance   | -0.0181     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 2.71        |\n",
            "|    n_updates            | 140         |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 7.09        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 16         |\n",
            "|    time_elapsed         | 437        |\n",
            "|    total_timesteps      | 32768      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02443498 |\n",
            "|    clip_fraction        | 0.241      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.5      |\n",
            "|    explained_variance   | -0.0293    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.9        |\n",
            "|    n_updates            | 150        |\n",
            "|    policy_gradient_loss | -0.0277    |\n",
            "|    std                  | 1.03       |\n",
            "|    value_loss           | 8.48       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 17          |\n",
            "|    time_elapsed         | 464         |\n",
            "|    total_timesteps      | 34816       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016761096 |\n",
            "|    clip_fraction        | 0.233       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.5       |\n",
            "|    explained_variance   | -0.0188     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3           |\n",
            "|    n_updates            | 160         |\n",
            "|    policy_gradient_loss | -0.0162     |\n",
            "|    std                  | 1.03        |\n",
            "|    value_loss           | 9.8         |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 74         |\n",
            "|    iterations           | 18         |\n",
            "|    time_elapsed         | 491        |\n",
            "|    total_timesteps      | 36864      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.03664797 |\n",
            "|    clip_fraction        | 0.34       |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.6      |\n",
            "|    explained_variance   | 0.00438    |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 4.71       |\n",
            "|    n_updates            | 170        |\n",
            "|    policy_gradient_loss | -0.0206    |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 6.72       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 74          |\n",
            "|    iterations           | 19          |\n",
            "|    time_elapsed         | 518         |\n",
            "|    total_timesteps      | 38912       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.030327313 |\n",
            "|    clip_fraction        | 0.308       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.6       |\n",
            "|    explained_variance   | 0.0309      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6           |\n",
            "|    n_updates            | 180         |\n",
            "|    policy_gradient_loss | -0.00991    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 11.2        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 20          |\n",
            "|    time_elapsed         | 545         |\n",
            "|    total_timesteps      | 40960       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.028725432 |\n",
            "|    clip_fraction        | 0.244       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.7       |\n",
            "|    explained_variance   | 0.0224      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 1.66        |\n",
            "|    n_updates            | 190         |\n",
            "|    policy_gradient_loss | -0.00988    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 6.45        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 21          |\n",
            "|    time_elapsed         | 573         |\n",
            "|    total_timesteps      | 43008       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.035785355 |\n",
            "|    clip_fraction        | 0.252       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.8       |\n",
            "|    explained_variance   | -0.00199    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 7.69        |\n",
            "|    n_updates            | 200         |\n",
            "|    policy_gradient_loss | -0.00915    |\n",
            "|    std                  | 1.04        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "----------------------------------------\n",
            "| time/                   |            |\n",
            "|    fps                  | 75         |\n",
            "|    iterations           | 22         |\n",
            "|    time_elapsed         | 600        |\n",
            "|    total_timesteps      | 45056      |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 0.02488321 |\n",
            "|    clip_fraction        | 0.259      |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -43.8      |\n",
            "|    explained_variance   | 0.0224     |\n",
            "|    learning_rate        | 0.00025    |\n",
            "|    loss                 | 3.55       |\n",
            "|    n_updates            | 210        |\n",
            "|    policy_gradient_loss | -0.00496   |\n",
            "|    std                  | 1.04       |\n",
            "|    value_loss           | 8.94       |\n",
            "----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 23          |\n",
            "|    time_elapsed         | 627         |\n",
            "|    total_timesteps      | 47104       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.024153344 |\n",
            "|    clip_fraction        | 0.272       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -43.9       |\n",
            "|    explained_variance   | -0.0405     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.38        |\n",
            "|    n_updates            | 220         |\n",
            "|    policy_gradient_loss | -0.0153     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 14.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 24          |\n",
            "|    time_elapsed         | 654         |\n",
            "|    total_timesteps      | 49152       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.037768982 |\n",
            "|    clip_fraction        | 0.309       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44         |\n",
            "|    explained_variance   | -0.00958    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.37        |\n",
            "|    n_updates            | 230         |\n",
            "|    policy_gradient_loss | 0.00366     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 12.4        |\n",
            "-----------------------------------------\n",
            "day: 3650, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3250700.91\n",
            "total_reward: 2250700.91\n",
            "total_cost: 329606.76\n",
            "total_trades: 98701\n",
            "Sharpe: 0.750\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 75          |\n",
            "|    iterations           | 25          |\n",
            "|    time_elapsed         | 681         |\n",
            "|    total_timesteps      | 51200       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.034196474 |\n",
            "|    clip_fraction        | 0.301       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -44.1       |\n",
            "|    explained_variance   | -0.00227    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.84        |\n",
            "|    n_updates            | 240         |\n",
            "|    policy_gradient_loss | -0.0192     |\n",
            "|    std                  | 1.05        |\n",
            "|    value_loss           | 11.4        |\n",
            "-----------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "000d49bb-5566-41a2-b2fa-1012d8f5db43"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "617083d0-d21d-40e1-b7ac-1b9b403ca355"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_1\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 26       |\n",
            "|    time_elapsed    | 559      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 4.64     |\n",
            "|    critic_loss     | 720      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 10953    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 22       |\n",
            "|    time_elapsed    | 1289     |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 10.6     |\n",
            "|    critic_loss     | 14.2     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 25557    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3319811.55\n",
            "total_reward: 2319811.55\n",
            "total_cost: 998.99\n",
            "total_trades: 51100\n",
            "Sharpe: 0.649\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "98852a78-1426-40cf-a7c4-f74490940a70"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 1000000,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "857cb26c-81af-41a0-df39-c57942be7909"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_2\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 21       |\n",
            "|    time_elapsed    | 685      |\n",
            "|    total timesteps | 14604    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 172      |\n",
            "|    critic_loss     | 28.6     |\n",
            "|    ent_coef        | 0.0742   |\n",
            "|    ent_coef_loss   | -126     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 14503    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 1401     |\n",
            "|    total timesteps | 29208    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 9.68     |\n",
            "|    critic_loss     | 9.81     |\n",
            "|    ent_coef        | 0.0174   |\n",
            "|    ent_coef_loss   | -173     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 29107    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4889674.97\n",
            "total_reward: 3889674.97\n",
            "total_cost: 7706.97\n",
            "total_trades: 70158\n",
            "Sharpe: 0.752\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 2114     |\n",
            "|    total timesteps | 43812    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -13.3    |\n",
            "|    critic_loss     | 14       |\n",
            "|    ent_coef        | 0.00427  |\n",
            "|    ent_coef_loss   | -128     |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 43711    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 2842     |\n",
            "|    total timesteps | 58416    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -7       |\n",
            "|    critic_loss     | 8.71     |\n",
            "|    ent_coef        | 0.00148  |\n",
            "|    ent_coef_loss   | -3.87    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 58315    |\n",
            "---------------------------------\n",
            "day: 3650, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3389166.27\n",
            "total_reward: 2389166.27\n",
            "total_cost: 1895.61\n",
            "total_trades: 62481\n",
            "Sharpe: 0.623\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 20       |\n",
            "|    time_elapsed    | 3585     |\n",
            "|    total timesteps | 73020    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -4.82    |\n",
            "|    critic_loss     | 12.4     |\n",
            "|    ent_coef        | 0.00159  |\n",
            "|    ent_coef_loss   | -4.38    |\n",
            "|    learning_rate   | 0.0001   |\n",
            "|    n_updates       | 72919    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be greater than the maximum of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "source": [
        "data_turbulence = processed_full[(processed_full.date<'2019-01-01') & (processed_full.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "6dd6e5d6-38d1-4f60-ae70-1168757eb1fc"
      },
      "source": [
        "insample_turbulence.turbulence.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       33.278197\n",
              "std        33.999888\n",
              "min         0.000000\n",
              "25%        15.233650\n",
              "50%        25.166725\n",
              "75%        39.289944\n",
              "max       332.850050\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwoz_7VSh1jO",
        "outputId": "a22d3e00-8a19-4b18-dd3e-9d072a0f6aca"
      },
      "source": [
        "turbulence_threshold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "332.8500501393391"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "source": [
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "e_trade_gym = StockTradingEnv(df = trade, **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "b1422836-7962-4847-e6d1-492665a8f685"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>OPM</th>\n",
              "      <th>NPM</th>\n",
              "      <th>ROA</th>\n",
              "      <th>ROE</th>\n",
              "      <th>cur_ratio</th>\n",
              "      <th>quick_ratio</th>\n",
              "      <th>cash_ratio</th>\n",
              "      <th>inv_turnover</th>\n",
              "      <th>acc_rec_turnover</th>\n",
              "      <th>acc_pay_turnover</th>\n",
              "      <th>debt_ratio</th>\n",
              "      <th>debt_to_equity</th>\n",
              "      <th>PE</th>\n",
              "      <th>PB</th>\n",
              "      <th>Div_yield</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.439735</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>0.258891</td>\n",
              "      <td>0.227773</td>\n",
              "      <td>0.133360</td>\n",
              "      <td>0.430843</td>\n",
              "      <td>1.315382</td>\n",
              "      <td>1.134347</td>\n",
              "      <td>0.854114</td>\n",
              "      <td>23.571867</td>\n",
              "      <td>7.620024</td>\n",
              "      <td>3.781658</td>\n",
              "      <td>0.690466</td>\n",
              "      <td>2.230663</td>\n",
              "      <td>5.737274</td>\n",
              "      <td>1.672991</td>\n",
              "      <td>0.018991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>91.803406</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>0.203479</td>\n",
              "      <td>0.160494</td>\n",
              "      <td>0.026811</td>\n",
              "      <td>0.237960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.231669</td>\n",
              "      <td>0.279424</td>\n",
              "      <td>0.887329</td>\n",
              "      <td>7.875371</td>\n",
              "      <td>50.720114</td>\n",
              "      <td>3.458432</td>\n",
              "      <td>0.004248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>0.116496</td>\n",
              "      <td>0.102682</td>\n",
              "      <td>0.066409</td>\n",
              "      <td>34.409483</td>\n",
              "      <td>1.070490</td>\n",
              "      <td>0.262465</td>\n",
              "      <td>0.092436</td>\n",
              "      <td>0.933164</td>\n",
              "      <td>5.468453</td>\n",
              "      <td>4.151637</td>\n",
              "      <td>0.998070</td>\n",
              "      <td>517.142241</td>\n",
              "      <td>83.019826</td>\n",
              "      <td>1418.196271</td>\n",
              "      <td>0.006531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>118.137177</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>0.186871</td>\n",
              "      <td>0.107064</td>\n",
              "      <td>0.056932</td>\n",
              "      <td>0.289572</td>\n",
              "      <td>1.428582</td>\n",
              "      <td>0.919490</td>\n",
              "      <td>0.266175</td>\n",
              "      <td>2.135008</td>\n",
              "      <td>2.339630</td>\n",
              "      <td>3.660183</td>\n",
              "      <td>0.803394</td>\n",
              "      <td>4.086316</td>\n",
              "      <td>35.907956</td>\n",
              "      <td>4.375155</td>\n",
              "      <td>0.007280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>39.496738</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>0.263373</td>\n",
              "      <td>0.261680</td>\n",
              "      <td>0.098017</td>\n",
              "      <td>0.246218</td>\n",
              "      <td>1.801859</td>\n",
              "      <td>1.677431</td>\n",
              "      <td>1.370671</td>\n",
              "      <td>7.722516</td>\n",
              "      <td>4.244056</td>\n",
              "      <td>7.937160</td>\n",
              "      <td>0.601911</td>\n",
              "      <td>1.512001</td>\n",
              "      <td>28.011871</td>\n",
              "      <td>4.283841</td>\n",
              "      <td>0.008355</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date   tic        open  ...         PE           PB  Div_yield\n",
              "0 2019-01-01  AAPL   38.722500  ...   5.737274     1.672991   0.018991\n",
              "0 2019-01-01   AXP   93.910004  ...  50.720114     3.458432   0.004248\n",
              "0 2019-01-01    BA  316.190002  ...  83.019826  1418.196271   0.006531\n",
              "0 2019-01-01   CAT  124.029999  ...  35.907956     4.375155   0.007280\n",
              "0 2019-01-01  CSCO   42.279999  ...  28.011871     4.283841   0.008355\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLOnL5eYh1jR",
        "outputId": "ceac333c-fb0b-45f8-b4fb-a6dbe686dfc8"
      },
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "6a0e83ec-64b6-4c1b-d25d-e73fbdada5e5"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(731, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2yRkNguY5yvp",
        "outputId": "0a83ec74-fa3f-45a4-d6b9-192393a0c47d"
      },
      "source": [
        "df_account_value.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>1.399637e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>1.399637e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>1.395260e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>1.403017e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>1.413811e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date  account_value\n",
              "726 2020-12-27   1.399637e+06\n",
              "727 2020-12-28   1.399637e+06\n",
              "728 2020-12-29   1.395260e+06\n",
              "729 2020-12-30   1.403017e+06\n",
              "730 2020-12-31   1.413811e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "cdcbfb6d-4789-4fe4-a525-be08ad4f671c"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-01</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-05</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL  AXP  BA  CAT  CSCO  CVX  DD  ...  TRV  UNH  V  VZ  WBA  WMT  XOM\n",
              "date                                           ...                                \n",
              "2019-01-01     0  100   0  100     0    0   0  ...    0    0  0   0  100  100  100\n",
              "2019-01-02     0  100   0  100     0    0   0  ...    0    0  0   0  100  100  100\n",
              "2019-01-03     0  100   0  100     0    0   0  ...    0    0  0   0  100  100  100\n",
              "2019-01-04     0  100   0  100     0    0   0  ...    0    0  0   0  100  100  100\n",
              "2019-01-05     0  100   0  100     0    0   0  ...    0    0  0   0  100  100  100\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "bb49bf35-ebd0-4937-851e-bf3d7550f724"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.126795\n",
            "Cumulative returns     0.413811\n",
            "Annual volatility      0.221206\n",
            "Sharpe ratio           0.651147\n",
            "Calmar ratio           0.386444\n",
            "Stability              0.322124\n",
            "Max drawdown          -0.328107\n",
            "Omega ratio            1.179533\n",
            "Sortino ratio          0.927539\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.907758\n",
            "Daily value at risk   -0.027298\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "4a0a8668-8762-415f-d176-f684af5b70e4"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = '2019-01-01',\n",
        "        end = '2021-01-01')\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n",
            "Annual return          0.144674\n",
            "Cumulative returns     0.310981\n",
            "Annual volatility      0.274619\n",
            "Sharpe ratio           0.631418\n",
            "Calmar ratio           0.390102\n",
            "Stability              0.116677\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.149365\n",
            "Sortino ratio          0.870084\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.860710\n",
            "Daily value at risk   -0.033911\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "270a0ad9-e5fd-4ac3-94a5-83247fc30577"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2021-01-01')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>17.866%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>39.015%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>22.651%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-23.429%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>20.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.778%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        {
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            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23.43</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-06-05</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.45</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>2020-12-11</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.87</td>\n",
              "      <td>2019-07-15</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-09-11</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.79</td>\n",
              "      <td>2019-09-18</td>\n",
              "      <td>2019-10-02</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.28</td>\n",
              "      <td>2019-05-16</td>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>2019-06-06</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzBaE63H3RLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}